LoneStriker
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Upload folder using huggingface_hub
Browse files- README.md +104 -0
- config.json +26 -0
- generation_config.json +6 -0
- model.safetensors.index.json +298 -0
- output.safetensors +3 -0
- special_tokens_map.json +28 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +46 -0
README.md
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---
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base_model: mlabonne/Marcoro14-7B-slerp
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license: apache-2.0
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datasets:
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- argilla/distilabel-intel-orca-dpo-pairs
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---
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# Model Card for decruz07/kellemar-DPO-Orca-Distilled-7B
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<!-- Provide a quick summary of what the model is/does. -->
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This model was created using mlabonne/Marcoro14-7B-slerp as the base, and finetuned with argilla/distilabel-intel-orca-dpo-pairs
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## Model Details
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Finetuned with these specific parameters:
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Steps: 200
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Learning Rate: 5e5
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Beta: 0.1
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** @decruz
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- **Funded by [optional]:** my full-time job
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- **Finetuned from model [optional]:** mlabonne/Marcoro14-7B-slerp
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## Benchmarks
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Top 5 in OpenLLM Benchmarks as of 2024/01/17
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**OpenLLM**
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|Model| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
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|---|---|---|---|---|---|---|---|
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|**kellemar-DPO-Orca-Distilled-7B-SLERP**| 73.71 | 70.48 | 87.56 | 65.33 |64.97 | 81.93 | 72.02 |
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**Nous**
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Model| AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
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|---|---|---|---|---|---|
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|**kellemar-DPO-Orca-Distilled-7B-SLERP**| 45.27 | 76.42 | 65.48 | 47.21 |58.6 |
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|Marcoro14-7B-slerp| 44.66 | 76.24 | 64.15 | 45.64 |57.67 |
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|kellemar-DPO-Orca-Distilled-7B| 43.61 | 73.14 | 55.73 | 42.28 |53.69 |
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|kellemar-Orca-DPO-7B| 43.35 | 73.43 | 54.02 | 42.24 |53.26 |
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|OpenHermes-2.5-Mistral-7B| 43.07 | 73.12 | 53.04 | 40.96 |52.38 |
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## Uses
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You can use this for basic inference. You could probably finetune with this if you want to.
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## How to Get Started with the Model
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You can create a space out of this, or use basic python code to call the model directly and make inferences to it.
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[More Information Needed]
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## Training Details
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The following was used:
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`training_args = TrainingArguments(
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per_device_train_batch_size=4,
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gradient_accumulation_steps=4,
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gradient_checkpointing=True,
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learning_rate=5e-5,
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lr_scheduler_type="cosine",
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max_steps=200,
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save_strategy="no",
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logging_steps=1,
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output_dir=new_model,
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optim="paged_adamw_32bit",
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warmup_steps=100,
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bf16=True,
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report_to="wandb",
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)
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# Create DPO trainer
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dpo_trainer = DPOTrainer(
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model,
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ref_model,
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args=training_args,
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train_dataset=dataset,
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tokenizer=tokenizer,
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peft_config=peft_config,
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beta=0.1,
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max_prompt_length=1024,
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max_length=1536,
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)`
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### Training Data
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This was trained with https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
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### Training Procedure
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Trained with Labonne's Google Colab Notebook on Finetuning Mistral 7B with DPO.
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## Model Card Authors [optional]
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@decruz
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## Model Card Contact
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@decruz on X/Twitter
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config.json
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{
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"_name_or_path": "mlabonne/Marcoro14-7B-slerp",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.36.2",
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"use_cache": true,
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"vocab_size": 32000
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.36.2"
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}
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model.safetensors.index.json
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